The present invention relates generally to new, improved, and reliable systems, methods, and techniques for the detection of leaks in the tubes of industrial boilers, including those of the types used by utilities to produce steam for electric power production.
Boiler Tube Leak Detection
Because of heat, pressure, and wear over time, boiler tubes eventually begin to leak, i.e., the beginning of a "leak event." When a boiler tube(s) starts to leak, steam which flashes over from the water escaping through the leak therein is lost to the boiler environment. In general, the amount of leaked water/steam may be small at the inception of a tube leak event. However, unless the tube is repaired, the leak will continue to grow, i.e., the tube leak rate increases with time until the tube eventually ruptures. Once such rupture occurs the utility operating such boiler is forced to shut it down immediately.
Boiler tube failures are a major cause of forced shut downs in fossil power plants. For example, approximately 41,000 tube failures occur every year in the United States alone. The cost of these failures proves to be quite expensive for utilities, exceeding $5 billion a year. [Lind, M. H., "Boiler Tube Leak Detection System," Proceedings of the Third EPRI Incipient-Failure Detection Conference, EPRI CS-5395, March 1987]
In order to reduce the occurrences of such forced outages, early boiler tube leak detection is highly desirable. Early boiler tube leak detection would allow utilities to schedule a repair rather than to suffer a later forced outage. In addition, the earlier the detection, the better the chances are of limiting damage to adjacent tubes.
Artificial Neural Networks
Artificial neural networks (ANNs) are information-processing models inspired by the architecture of the human brain. ANNs are capable of learning and generalization and are model-free adaptive estimators of maps (relations between the input and the output of the ANN, or, as later referenced, an inference engine) which learn using example data. As is discussed in the prior art, including the patent literature, when a neural network is to be used in detection applications, it is necessary to execute beforehand a learning procedure for establishing suitable parameter values within the ANN. In the learning procedure, a set of sample patterns (referred to herein as the learning data), which have been selected in accordance with the patterns which are to be recognized, are successively inputted to the ANN. For each sample pattern there is a known appropriate output pattern, i.e. a pattern which should be produced from the network in response to that input pattern. The required known output patterns are referred to as the teaching data. In the learning procedure, the learning data patterns are successively supplied to the ANN, and resultant output patterns produced from the ANN are compared with the corresponding teaching data patterns, to obtain respective amounts of recognition error. The internal parameters of the ANN are successively adjusted in accordance with these sequentially obtained amounts of error, using a suitable learning algorithm. These operations are repetitively executed for the set of learning data, until a predetermined degree of convergence towards a maximum degree of pattern recognition is achieved (i.e., the maximum that can be achieved by using that particular set of learning data). The degree of recognition can be measured as a recognition index, expressed, for example, as a percentage.
The greater the number of sample patterns constituting the learning data, the greater will be the invariant characteristic information that is learned by the ANN. Alternatively stated, a learning algorithm which is utilized in such a procedure (i.e. for adjusting the ANN internal parameters in accordance with the error amounts obtained during the learning procedure) attempts to achieve learning of a complete set of probability distributions of a statistical population, i.e. a statistical population which consists of data, consisting of all of the possible patterns which the ANN will be required to recognize after learning has been achieved. That is to say, the learning algorithm performs a kind of pre-processing, prior to actual pattern recognition operation being started, whereby characteristics of the patterns which are to be recognized are extracted and applied to modify the internal parameters of the ANN.
In the practice of the prior art it has been necessary to utilize as large a number of sample data in the learning procedure as possible, in order to maximize the recognition index which is achieved for a ANN. However, there are practical limitations on the number of sample patterns which can be stored in memory for use as learning data. Furthermore, such learning data may include data which will actually tend to lower the recognition index, if used in the learning procedure. Accordingly, and as will be better appreciated after reading and understanding the more detailed description below, the decentralized architecture or structure of the instant new detection system and the staging of testing significantly overcomes such prior art related disadvantages.
ANNs can be divided into two classes: feed-forward and feedback neural networks. Within each class, ANNs are also characterized by the number of hidden layers, number of neurons in a given layer, and the method of learning. While many different types of learning are available, the back propagation learning algorithm (BPLA) is of the most interest to the practice of the instant invention. The BPLA is an error-correcting learning procedure which uses the gradient descent method to adjust the synaptic weights. BPLA is intended for ANNs with an input layer, any number of hidden layers, and an output layer. In the most preferred embodiments of the instant invention, the ANNs used are feed forward and possess two hidden layers. Other types of ANNs with different topologies and learning algorithms can be used as well. As will be better appreciated from the teachings and discussions found infra, the first two embodiments of the instant invention, i.e. embodiments one and two utilize ANNs to effect the desired and necessary learning and decision making for early detection of boiler tube leak events.
Fuzzy Logic
Exact models of dynamical systems become increasingly difficult to obtain if not impossible as system complexity increases. This fact is summarized by what Zadeh, infra, called the principle of incompatibility: "as the complexity of a system increases, our ability to make precise and yet significant statements about its behavior diminishes until a threshold is reached beyond which precision and significance (or relevance) become almost mutually exclusive characteristics." [L. A. Zadeh, "A theory of approximate reasoning," in J. Hayes, D. Michie, and L. I. Mikulich, (eds.), Machine Intelligence, Vol. 9, Halstead Press: New York, SMC-3, 1979]
The uncertainty in the knowledge about real-world systems and their dynamic models has motivated the application of fuzzy set theory to handle real world problems. [L. A. Zadeh, "Fuzzy algorithms," Information and Control, Vol. 12, 1968] [D. Dubois and H. Pradc, "Fuzzy Sets and Systems: Theory and Applications," Academic, Orlando, Fla., 1980] This motivation stems from the fact that fuzzy set theory provides a suitable representation of the uncertainty in system knowledge and dynamic models. In fuzzy set theory the reasoning in the face of uncertain information, called approximate reasoning, employs fuzzy logic as a framework for uncertain information processing and inference. [R. R. Yager and D. P. Filev, "Essentials of Fuzzy Modeling and Control," Wiley Interscience, New York, 1994] Fuzzy set theory is an approach useful for presenting and utilizing linguistic "qualitative" descriptions in computerized inference which improves the potential to model human reasoning in an inexact and uncertain domain in cases where statistical information is not available. The concept of possibility may be used to model the confidence level of various hypotheses by a number between zero and one, where one may be the highest degree of confidence and zero the lowest, or vice versa. In order to quantify inexactness, fuzzy set theory utilizes the notion of a membership function in terms of the level of confidence that a particular element belongs to a particular fuzzy set. Given the complexity of boiler tube leak events, it will be appreciated by those skilled in this art that there exists substantial motivation to utilize fuzzy logic in attempting to effect the detection of boiler tube leaks at the earliest possible moment by a technique which looks for an approximate or "fuzzy" map, between tube leak events and the sensitive variables, supra, and thereafter utilize approximate reasoning for detecting the occurrence and location of boiler tube leaks. Accordingly, the second two embodiments of the instant invention, i.e. embodiments three and four, integrate ANNs and fuzzy logic to effect the desired early detection of boiler tube leaks.